pyepo.func.jax.rank
===================

.. py:module:: pyepo.func.jax.rank

.. autoapi-nested-parse::

   Learning to rank Losses



Attributes
----------

.. autoapisummary::

   pyepo.func.jax.rank.lsLTR
   pyepo.func.jax.rank.prLTR
   pyepo.func.jax.rank.ptLTR


Classes
-------

.. autoapisummary::

   pyepo.func.jax.rank.listwiseLearningToRank
   pyepo.func.jax.rank.pairwiseLearningToRank
   pyepo.func.jax.rank.pointwiseLearningToRank


Module Contents
---------------

.. py:class:: listwiseLearningToRank(optmodel, processes=1, solve_ratio=1.0, reduction: pyepo.func.runtime.Reduction = 'mean', dataset=None)

   Bases: :py:obj:`pyepo.func.jax.abcmodule.optModule`


   Listwise Learning-to-Rank loss over a cached solution pool.

   Models the ranking distribution over the pool as a SoftMax of
   predicted-cost scores and minimizes its cross-entropy against the true
   ranking distribution.

   Reference: Mandi et al. (2022)
   `<https://proceedings.mlr.press/v162/mandi22a.html>`_


   .. py:method:: forward(pred_cost, true_cost)

      Forward pass



.. py:class:: pairwiseLearningToRank(optmodel, processes=1, solve_ratio=1.0, reduction: pyepo.func.runtime.Reduction = 'mean', dataset=None)

   Bases: :py:obj:`pyepo.func.jax.abcmodule.optModule`


   Pairwise Learning-to-Rank loss over a cached solution pool.

   Enforces a margin between the true optimum (best pool member) and each
   suboptimal solution via a ReLU hinge on the predicted-cost difference.

   Reference: Mandi et al. (2022)
   `<https://proceedings.mlr.press/v162/mandi22a.html>`_


   .. py:method:: forward(pred_cost, true_cost)

      Forward pass



.. py:class:: pointwiseLearningToRank(optmodel, processes=1, solve_ratio=1.0, reduction: pyepo.func.runtime.Reduction = 'mean', dataset=None)

   Bases: :py:obj:`pyepo.func.jax.abcmodule.optModule`


   Pointwise Learning-to-Rank loss over a cached solution pool.

   Fits the predicted score of each pool member toward its true score by
   squared error, averaged over the pool.

   Reference: Mandi et al. (2022)
   `<https://proceedings.mlr.press/v162/mandi22a.html>`_


   .. py:method:: forward(pred_cost, true_cost)

      Forward pass



.. py:data:: lsLTR

.. py:data:: prLTR

.. py:data:: ptLTR